Performance evaluation, scale‐up design, and economic analysis for remediation of iron‐containing wastewater using cow manure biocarbon
Bibliographic record
Abstract
Abstract Excessive iron in wastewater poses a significant threat to aquatic ecosystems due to its toxic effects on aquatic life and its contribution to oxygen depletion. When applied to cropland, iron‐containing wastewater leads to soil acidification and reduces phosphorus availability, thereby impacting agricultural productivity. Addressing this issue requires techno‐economically viable remediation strategies. This study investigates cow manure biocarbon as a sustainable adsorbent for iron sequestration from wastewater. Batch experiments using synthetic solutions with 10–50 mg L −1 iron examined the influence of adsorbent dose, pH, and contact duration on iron removal efficiency. The cow manure biocarbon was characterized for evaluation of its physicochemical attributes. Adsorption achieved nearly 84% removal efficiency under optimal conditions of 120 min contact time, pH 9, and 0.25 g adsorbent dosage at 30°C. Adsorption isotherm data were modelled using the Langmuir and Freundlich models, with the Langmuir isotherm providing the best fit, as indicated by a low SSE (0.7056), RMSE (0.84), χ 2 (0.0044), and a high R 2 value of 0.995. Kinetic data were evaluated using pseudo first order and pseudo second order models, revealing that the process followed pseudo first order kinetics, evidenced by a low SSE (0.0144), RMSE (0.12), χ 2 (0.0044), and a high R 2 value of 0.9888. The adsorbent demonstrated good reusability and stability over five regeneration cycles. To assess the real‐world application of this process, a theoretical scale‐up design for wastewater remediation was developed. Furthermore, an economic and preliminary life cycle assessment was conducted to evaluate cost‐effectiveness and sustainability aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".